Improving the accuracy of environment-specific vehicular channel modeling

Wang, Xiaohui; Anderson, Eric; Steenkiste, Peter; Bai, Fan

doi:10.1145/2348688.2348699

Cited by 16 publications

(15 citation statements)

References 16 publications

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“…Therefore, the results we obtain mostly pertain to buildings and vehicles. However, as described by Wang et al in [18], high-precision foliage maps (1m x 1m resolution) can be extracted using image classification techniques [19] on freely available aerial photography data [15], [20]. We used regular passenger cars and commercial vehicles depicted in Fig.…”

Section: Experiments Setupmentioning

confidence: 99%

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Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Boban¹,

Barros

Tonguz

2014

IEEE Trans. Veh. Technol.

208

145

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Large-scale Vehicular Ad Hoc Network (VANET) simulators by and large employ simple statistical channel models. By design, such models do not account for specific objects in the region of interest when estimating the channel. While computationally efficient, these models were shown to be unable to provide satisfactory accuracy on a link level for typical VANET scenarios. Specifically, experimental studies have shown that both large static objects (e.g., buildings and foliage) as well as mobile objects (surrounding vehicles) have a profound impact on the quality of vehicle-to-vehicle (V2V) channels. While several recently proposed large-scale V2V channel models account for static objects (e.g., buildings) in the simulated area, there is a need for a comprehensive model that takes into account both the static and the mobile objects. To fill this gap, we designed a geometry-based, computationally manageable V2V channel model that uses the real-world locations and the actual dimensions of vehicles, buildings, and foliage to simulate the V2V channel more realistically. We use the outlines of the modeled objects to form spatial tree structures for efficient manipulation of geographic data. We distinguish and model separately the following three link types: line of sight (LOS), non-LOS due to vehicles, and non-LOS due to static objects. Apart from the model for large-scale signal variations, we also propose a simple model for small-scale signal variation using the number and size of the objects around the communicating vehicles. We validate the models against extensive measurements performed in urban, suburban, highway, and open space environment. We provide the complete simulation recipe for the implementation of the model in simulators. Finally, we implement the model in Matlab and show that it scales well by simulating networks with tens of thousands of objects and hundred thousand communicating vehicle pairs using commodity hardware.

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Section: Experiments Setupmentioning

confidence: 99%

“…With regards to improving the channel modeling using location-specific information, Wang et al in [18] utilize aerial photography to determine the density of scatterers in the simulated area. By processing the aerial data to infer the scatterer density, the authors determine the fading level for a given location on the road.…”

Section: Related Workmentioning

confidence: 99%

Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Boban¹,

Barros

Tonguz

2014

IEEE Trans. Veh. Technol.

208

145

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“…Refs. [16,17] presented a general framework for modeling and reproducing environment-specific channel properties in V2V (Vehicle-to-Vehicle) communication. In contrast to the traditional deterministic models or stochastic models, these new models try to introduce some deterministic pattern or feature into the stochastic models to obtain extra information with the purpose of acquiring CSI acquisition at a lower cost.…”

Section: Journal Of Communications and Information Networkmentioning

confidence: 99%

Wireless channel feature extraction via GMM and CNN in the tomographic channel model

Zhou

et al. 2017

J. Commun. Inf. Netw.

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Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research. The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI (Channel State Information). This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment. The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model. With this model, the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements, which can be defined as the wireless channel feature. In the proposed scheme for obtaining CSI with the tomographic channel model, the GMM (Gaussian Mixture Model) is applied to acquire the distribution of the wireless channel parameters, and the CNN (Convolutional Neural Network) is applied to automatically distinguish different wireless channels. The wireless channel feature information can be stored offline to guide the design of pilot symbols and save pilot resources. The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources. Thus, computing and storage resources can be exchanged for the finite and precious spectrum resource.Keywords: wireless channel modeling, tomographic channel model, Gaussian mixture model, convolutional neural network

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“…A subset of this work was previously published as [13]. In that paper, we described our layered simulation framework for V2V channels, and introduced our practical and efficient way Additional contributions in this paper are:…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Environment-Specific Channel Modeling

Wang

Anderson

Steenkiste

et al. 2016

IEEE Trans. on Mobile Comput.

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Networking research benefits from controlled, repeatable experimentation using simulation and emulation systems. Making simulations realistic is a challenge for wireless systems and is especially difficult for vehicular networks. This paper presents a general framework for modeling and reproducing environment-specific channel properties. We show that one can estimate localized environment information, and adding such information to state-of-the art channel models significantly increases accuracy.We describe the proposed framework and validate it for fading and line-of-sight effects in vehicle-to-vehicle channels. While suitable models can provide a close approximation of channel conditions, accuracy is limited by the quality of the input information about the environment being modeled. We present a systematic approach to estimating location-specific scattering properties using aerial photography. Using signal-level channel emulation, we show that the improved fading models produce more accurate results at the packet/link level. The error rates of the improved model are 45% and 22% lower than using previous state of the art, for Doppler spectrum similarity and packet delivery ratio, respectively.The proposed models -and their implementation -have been designed to minimize run-time complexity while preserving accuracy. The implementation is efficient enough to allow concurrent real-time simulation of many of channels.

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Improving the accuracy of environment-specific vehicular channel modeling

Cited by 16 publications

References 16 publications

Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Wireless channel feature extraction via GMM and CNN in the tomographic channel model

Improving the Accuracy of Environment-Specific Channel Modeling

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